| Project/Area Number |
18K18566
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 7:Economics, business administration, and related fields
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Inoue Kotaro 東京工業大学, 工学院, 教授 (90381904)
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| Co-Investigator(Kenkyū-buntansha) |
中田 和秀 東京工業大学, 工学院, 教授 (00312984)
池田 直史 日本大学, 法学部, 准教授 (90725243)
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| Project Period (FY) |
2018-06-29 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2018: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | テキストマイニング / 有価証券報告書 / ファイナンス / 開示制度 / 市場流動性 / M&A / リスクプレミアム / 開示制度変更 / Word Embedding / テキスト分析 / 経営財務 / 企業行動分析 |
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| Outline of Final Research Achievements |
In this study, textual information in corporate annual reports was analyzed using natural language processing to test whether textual data has valuable information that quantitative data does not. As a result of the analysis, we found that textual information in annual reports can predict the occurrence of future M&A with higher probability than quantitative data alone. The "Business and Other Risks" section of the annual reports have explanatory power with respect to the company's stock price in the following fiscal year. The changes in the content of annual reports based on the Cabinet Office Ordinance on Enhancing the Contents of Annual Reports have the effect of mitigating the information asymmetry between firms and investors, and contribute to improving stock liquidity.
The above series of studies were published as two peer-reviewed papers and two invited papers in academic journals.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は、従来の会計数値などの定量的データの充実に加え、テキストデータの充実が、企業と投資家の間の情報の非対称性の緩和に貢献し、株式市場の流動性改善につながることを示した。自然言語処理による企業開示情報の分析が市場効率性や事業戦略の予測に貢献することを示した点は学術的な意義がある。また、企業の開示情報における定性的テキスト情報の充実が、会計数値などでは観測できない情報を投資家に提供することを示し、制度的な対応の意義があることを示した点で今後の制度設計に有用な情報を提供しており、社会的意義がある。
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